The overall purpose of the Development Psychopathology Module is to elucidate the ontogeny of psychopathological characteristics in relation to substance use disorder (SUD) liability and outcome. During the proposed funding period, the hypotheses tested will focus primarily on the adolescent period. The development of SUD during adolescence is increasingly recognized as having important clinical and public health implications. Studies of children at high risk for SUD and adolescent with SUD, including CEDAR findings and those of affiliated projects, have noted the predominance of two domains of psychopathology; antisocial behavior disorders and negative affect disorders. The co-occurrence of these disorders with SUD strongly suggests the presence of a common liability hypothesized to be behavioral and emotional dysregulation. The Developmental Psychopathology Module proposes to prospectively examine the development of psychopathological characteristics in relation to SUD liability from childhood through late adolescence (i.e., age 19 years), determine the influence of childhood psychopathology on adolescent drug use and SUD, and determine the extent to which behavioral and emotional dysregulation prospectively predicts the acceleration of drug involvement leading to SUD. In association with other modules, the influence and interaction of these characteristics on involvement leading to SUD. In association with other modules, the influence and interaction of these characteristics on SUD liability and outcome will be examined in the context of genetically determined predispositions, neurobiological maturation, and environmental characteristics. Determining the influences of psychopathology on the origins and course of SUD is essential for understanding SUD etiology. The Developmental Psychopathology Module proposes to address this challenge through applying the conceptual framework of developmental psychopathology, operationalized through a prospective longitudinal assessment beginning in late childhood and innovative statistical modeling methods.